gwid

GWID (Genome Wide Identity by Descent) is an R-package designed for the analysis of IBD (Identity by Descent) data, to discover rare alleles associated with case-control phenotype. Although Genome Wide Association Studies (GWAS) successfully reveal numerous common variants linked to diseases, they exhibit lack of power to identify rare alleles. To address this limitation, we have developed a pipeline that employs IBD data (output of refined-IBD software). This methodology encompasses a sequential process for analyzing the aforementioned data within isolated populations. The primary objective of this approach is to enhance the sensitivity of variant detection by utilizing information from genetically related individuals, thereby facilitating the identification of causal variants. An overall representation of the pipeline is visually depicted in the following figure.

gwid pipeline

Usage

The gwid package receives four types of inputs: SNP panel information, IBD information, haplotype data, and data concerning subjects categorized as cases and controls. The SNP panel data is derived from the output of the SNPRelate package in the form of a gds file. The IBD data takes the form of tabulated data produced by the Refined IBD software. Haplotype data comes from the output of the Beagle, while information about case and control subjects is represented using an R list.

Installation

You can install the development version of gwid from GitHub with:

# install.packages("devtools")
devtools::install_github("soroushmdg/gwid")

Example

The following example is for a SNP panel data from the Marshfield Clinic. subjects in case group has Rheumatoid Arthritis (RA).

This is a basic example which shows you how to solve a common problem:

library(gwid)
#> 
#> Attaching package: 'gwid'
#> The following objects are masked from 'package:base':
#> 
#>     print, subset
caco <- gwid::case_control(case_control_rda = case_control_data)
pieces <- gwid::build_gwas(gds_data = genome_data,caco = caco,gwas_generator = TRUE)
myphase <- gwid::build_phase(phased_vcf = phase_data,caco = caco)
myregion2 <- gwid::build_gwid(ibd_data = ibd_data,gwas = pieces)
p <- plot(myregion2)
p

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

#summary(cars)

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/v1/examples.

You can also embed plots, for example:

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.